OCT A-Scan based lung tumor tissue classification with Bidirectional Long Short Term Memory networks

S. Otte; Christoph Otte; A. Schlaefer; L. Wittig; G. Huttmann; D. Dromann; A. Zeli

doi:10.1109/MLSP.2013.6661944

OCT A-Scan based lung tumor tissue classification with Bidirectional Long Short Term Memory networks

S. Otte^*, Christoph Otte, A. Schlaefer, L. Wittig, G. Huttmann, D. Dromann, A. Zeli

^*Korrespondierende/r Autor/-in für diese Arbeit

Eberhard Karls Universität Tübingen

Abstract

This paper presents a novel method for lung tumor tissue classification using Bidirectional Long Short Term Memory networks (BLSTMs). Samples are obtained through Optical Coherence Tomography (OCT) from real soft tissue specimen and represented as data sequences. Such sequences are learned with BLSTMs, which are able to recognize even non-uniformly compressed temporal encoded patterns in sequential data in both forward and backward time-direction. Our experiments indicate that BLSTMs are a suitable choice for this classification task, since they outperform other recurrent architectures. Furthermore, the presented findings lead to promising future investigations in the field of OCT based tissue analysis.

Originalsprache	Englisch
Titel	2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Herausgeber (Verlag)	IEEE
Erscheinungsdatum	2013
Aufsatznummer	6661944
ISBN (Print)	978-147991180-6
DOIs	https://doi.org/10.1109/MLSP.2013.6661944
Publikationsstatus	Veröffentlicht - 2013
Veranstaltung	2013 16th IEEE International Workshop on Machine Learning for Signal Processing - Southampton, Großbritannien / Vereinigtes Königreich Dauer: 22.09.2013 → 25.09.2013 Konferenznummer: 102379

Strategische Forschungsbereiche und Zentren

Forschungsschwerpunkt: Biomedizintechnik
Profilbereich: Lübeck Integrated Oncology Network (LION)
Zentren: Universitäres Cancer Center Schleswig-Holstein (UCCSH)

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10.1109/MLSP.2013.6661944

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Otte, S., Otte, C., Schlaefer, A., Wittig, L., Huttmann, G., Dromann, D., & Zeli, A. (2013). OCT A-Scan based lung tumor tissue classification with Bidirectional Long Short Term Memory networks. in 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) Artikel 6661944 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). IEEE. https://doi.org/10.1109/MLSP.2013.6661944

@inproceedings{51283578db6c412a932e9c118615cd50,

title = "OCT A-Scan based lung tumor tissue classification with Bidirectional Long Short Term Memory networks",

abstract = "This paper presents a novel method for lung tumor tissue classification using Bidirectional Long Short Term Memory networks (BLSTMs). Samples are obtained through Optical Coherence Tomography (OCT) from real soft tissue specimen and represented as data sequences. Such sequences are learned with BLSTMs, which are able to recognize even non-uniformly compressed temporal encoded patterns in sequential data in both forward and backward time-direction. Our experiments indicate that BLSTMs are a suitable choice for this classification task, since they outperform other recurrent architectures. Furthermore, the presented findings lead to promising future investigations in the field of OCT based tissue analysis.",

author = "S. Otte and Christoph Otte and A. Schlaefer and L. Wittig and G. Huttmann and D. Dromann and A. Zeli",

note = "Copyright: Copyright 2014 Elsevier B.V., All rights reserved.; 2013 16th IEEE International Workshop on Machine Learning for Signal Processing , MLSP 2013 ; Conference date: 22-09-2013 Through 25-09-2013",

year = "2013",

doi = "10.1109/MLSP.2013.6661944",

language = "English",

isbn = "978-147991180-6",

series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",

publisher = "IEEE",

booktitle = "2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)",

}

Otte, S, Otte, C, Schlaefer, A, Wittig, L, Huttmann, G , Dromann, D & Zeli, A 2013, OCT A-Scan based lung tumor tissue classification with Bidirectional Long Short Term Memory networks. in 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)., 6661944, IEEE International Workshop on Machine Learning for Signal Processing, MLSP, IEEE, 2013 16th IEEE International Workshop on Machine Learning for Signal Processing , Southampton, Großbritannien / Vereinigtes Königreich, 22.09.13. https://doi.org/10.1109/MLSP.2013.6661944

OCT A-Scan based lung tumor tissue classification with Bidirectional Long Short Term Memory networks. / Otte, S.; Otte, Christoph; Schlaefer, A. et al.
2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2013. 6661944 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP).

Publikation: Kapitel in Büchern/Berichten/Konferenzbänden › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - OCT A-Scan based lung tumor tissue classification with Bidirectional Long Short Term Memory networks

AU - Otte, S.

AU - Otte, Christoph

AU - Schlaefer, A.

AU - Wittig, L.

AU - Huttmann, G.

AU - Dromann, D.

AU - Zeli, A.

N1 - Conference code: 102379

PY - 2013

Y1 - 2013

N2 - This paper presents a novel method for lung tumor tissue classification using Bidirectional Long Short Term Memory networks (BLSTMs). Samples are obtained through Optical Coherence Tomography (OCT) from real soft tissue specimen and represented as data sequences. Such sequences are learned with BLSTMs, which are able to recognize even non-uniformly compressed temporal encoded patterns in sequential data in both forward and backward time-direction. Our experiments indicate that BLSTMs are a suitable choice for this classification task, since they outperform other recurrent architectures. Furthermore, the presented findings lead to promising future investigations in the field of OCT based tissue analysis.

AB - This paper presents a novel method for lung tumor tissue classification using Bidirectional Long Short Term Memory networks (BLSTMs). Samples are obtained through Optical Coherence Tomography (OCT) from real soft tissue specimen and represented as data sequences. Such sequences are learned with BLSTMs, which are able to recognize even non-uniformly compressed temporal encoded patterns in sequential data in both forward and backward time-direction. Our experiments indicate that BLSTMs are a suitable choice for this classification task, since they outperform other recurrent architectures. Furthermore, the presented findings lead to promising future investigations in the field of OCT based tissue analysis.

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-84893226563&origin=inward&txGid=a6ca0274500c7c964ad0cb53c5ee0523

U2 - 10.1109/MLSP.2013.6661944

DO - 10.1109/MLSP.2013.6661944

M3 - Conference contribution

SN - 978-147991180-6

T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP

BT - 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

PB - IEEE

T2 - 2013 16th IEEE International Workshop on Machine Learning for Signal Processing

Y2 - 22 September 2013 through 25 September 2013

ER -

Otte S, Otte C, Schlaefer A, Wittig L, Huttmann G , Dromann D et al. OCT A-Scan based lung tumor tissue classification with Bidirectional Long Short Term Memory networks. in 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE. 2013. 6661944. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). doi: 10.1109/MLSP.2013.6661944

OCT A-Scan based lung tumor tissue classification with Bidirectional Long Short Term Memory networks

Abstract

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